Cellular network-oriented improved reinforcement learning network coverage optimization method

Abstract

The invention discloses a cellular network-oriented improved reinforcement learning network coverage optimization method, which comprises the following steps of: (1) acquiring terminal drive test data and base station side static data from a heterogeneous wireless network environment, and processing to obtain a balanced data set; (2) selecting a part of data from the balanced data set as a training set, inputting the training set into a random forest model, and training the random forest model to obtain a network coverage prediction model; (3) setting a target function of coverage optimization; and (4) setting space mapping of reinforcement learning and network coverage optimization problems, and training a reinforcement learning agent to obtain an adjustment strategy of engineering parameters and a coverage optimization result. According to the method, the optimization behavior is automatically improved, so that the convergence speed is higher, meanwhile, a large amount of operation and maintenance optimization experience can be accumulated, an optimization strategy is autonomously formed, and the great influence of the optimization process on the network performance is avoided.

Description

technical field [0001] The invention relates to the technical field of communication networks, in particular to an improved reinforcement learning network coverage optimization method for cellular networks. Background technique [0002] With the rapid development of 5G mobile communication networks, the network structure is also changing. The performance requirements of the network itself and the requirements for user perception are constantly improving, which puts forward higher requirements for network operation and maintenance modes and methods. As the most basic network performance, network coverage directly affects user experience. [0003] The traditional network coverage performance analysis is mainly based on the theoretical propagation model in the planning tool, and simulates the site deployment environment to calculate the estimated coverage of the deployed site. This method based on the theoretical model has non-negligible deviations in actual situations. After ...

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Problems solved by technology

[0003] The traditional network coverage performance analysis is mainly based on the theoretical propagation model in the planning tool, and simulates the site deployment environment to calculate the estimated coverage of the deployed site. This method based on the theoretical model has non-negligible deviations in actual situations. After the site deployment and configuration are completed, it is often necessary to collect static data and measurement data from the network extensively, analyze the real performance of the network, and adjust the parameter configuration of the base station. With the expansion of network scale, the efficiency is getting lower and lower

Traditional methods have been unable to efficiently utilize limited network resources, so improving operation and maintenance efficiency has become an urgent problem in the field of mobile communications

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